Garment size mapping

ABSTRACT

Techniques for mapping size information associated with a client to target brands, garments, sizes, shapes, and styles for which there is no standardized correlation. The size information associated with a client may be generated by modeling client garments, accessing computer aided drawing (CAD) files associated with client garments, or by analyzing a history of garment purchases associated with the client. Information for target garments may be generated in a similar fashion. A system may then create a standardized scale with a set of sizes for a target, and map a client base size to that standardized size scale. Similar matching and mapping may also be done with shape and style considerations. A recommendation based on the mapping may then be communicated to the client.

CLAIM OF PRIORITY

This application is a Continuation of U.S filed. application Ser. No.16/167,867, filed Oct. 23, 2018, now U.S. Pat. No. 11,055,758, which isa Continuation of U.S. application Ser. No. 14/503,309, filed Sep. 30,2014, which are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present application relates generally to the technical field ofthree-dimensional (3-D) shape comparisons, and in particular toidentifying a size mappings and matches between a client and a garmentin an environment such as a cross-border transaction where standard sizeor shape translations are not readily available.

BACKGROUND

Shopping for clothes in conventional (e.g., non-online) can be anarduous task and, due to travelling and parking, can be very timeconsuming. With the advent of online shopping, consumers may purchaseclothing, while staying home, via a computer or any electronic deviceconnected to the Internet. Additionally, purchasing clothes online canbe different in comparison to purchasing clothes in a store. Onedifference is the lack of a physical dressing room to see if and how anarticle of clothing fits the particular consumer. Since differentconsumers can have different dimensions, seeing how an article ofclothing fits, by use of a dressing room, can be a very important aspectof a successful and satisfying shopping experience. Additionaldifficulties may arise in a cross-border or multi region transactionsituation, where a consumer may not be familiar with the sizingconventions used to describe a garment that the consumer is consideringpurchasing.

The systems and methods described in the present disclosure attempt toprovide solutions to the problems presented above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for garment matching, inaccordance with embodiments of the present disclosure.

FIG. 2 is a block diagram illustrating an exemplary file system, inaccordance with embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating an exemplary garment matchingmodule, in accordance with embodiments of the present disclosure.

FIG. 4 is a flow diagram of a process for size matching, according tocertain embodiments of the present disclosure.

FIG. 5 is a flow diagram of a process for size matching, according tocertain embodiments of the present disclosure.

FIG. 6 is a flow diagram of a process for size matching, according tocertain embodiments of the present disclosure.

FIG. 7 illustrates examples of garments templates and models in agarment database, in accordance with embodiments of the presentdisclosure.

FIG. 8 illustrates examples of garments templates and models in agarment database, in accordance with embodiments of the presentdisclosure.

FIG. 9 illustrates examples of garments templates and models in agarment database, in accordance with embodiments of the presentdisclosure.

FIG. 10 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DESCRIPTION OF EMBODIMENTS

Embodiments described herein related to three-dimensional (3-D) shapemapping and comparisons, and in particular to identifying a size matchbetween a client and a garment in an environment such as an onlinecross-border transaction where standard size or shape translations arenot readily available.

Purchasing in such a cross border transaction environment or when aconsumer is shopping in a different country or region may be difficultbecause of sizing differences, size differences, shape differences, orother differences between garments and garment description conventions.Embodiments described herein map sizes, shapes, brands, and other suchgarment details to a standardized size scale which may be compared withand presented to a user in terms of a user's “base” size or wardrobe.This may enable a user to make better purchasing decisions and minimizethe need for returns.

For example, a client in China who likes a certain brand in a certainsize can provide this information to an embodiment system. The systemmay analyze this information with other information about a targetgarment or target garment type, and may then provide the user withrecommendations for specific sizes, brands, designs, or other suchpurchase recommendations when the client is travelling or shopping inanother region with different sizing standards, such as the UnitedStates. In still further embodiments, style and shape information may beprovided to a system by a client. This information may be analyzed torecommend items that have similar style and shape characteristics basedon a direct analysis of the clothes, even when different terms orlanguages are used to describe the style in marketing, sales, andgeneral description information. Such analysis may further recommenditems that keep a user's base shape and style tastes intact while theuser shops in a different country, or may alert a user that they may notbe able to get items that exactly fit the user's preference while stillfitting in with the local garment source's styles and customs.

In addition to comparing a client's size and taste with garments forpotential purchase, systems may gather information about a client's sizeand about target garments in a variety of ways. Garment models may begenerated based on images or pictures of a physical garment. Computeraided design (CAD) files with details about certain garments may beaccessed. History data about purchases made by a client and by otherclients may also be used. Any and all of these types of information maybe gathered by a system, and standardized to map base information abouta client to information about target garments. Additional detailsrelated to both the gathering of garment information and the mappingprocesses are described below.

Examples merely typify possible variations. Unless explicitly statedotherwise, components and functions are optional and may be combined orsubdivided, and operations may vary in sequence or be combined orsubdivided. In the following description, for purposes of explanation,numerous specific details are set forth to provide a thoroughunderstanding of example embodiments. It will be evident to one skilledin the art, however, that the present subject matter may be practicedwithout these specific details.

Reference is made in detail to various embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure and the describedembodiments. However, the present disclosure may be practiced withoutthese specific details. In other instances, well-known methods,procedures, components, and circuits have not been described in detailso as not to unnecessarily obscure aspects of the embodiments.

FIG. 1 is a block diagram illustrating a system 100 in accordance withone embodiment of the present disclosure. The system 100 includes clientdevices (e.g., client device 10-1, client device 10-2, client device10-3) connected to server 202 via network 34 (e.g., the Internet).Server 202 as shown includes one or more processing units (CPUs) 222 forexecuting modules, programs and/or instructions stored in memory 236 andthereby performing processing operations; one or more communicationsinterfaces 220; memory 236; and one or more communication buses 230 forinterconnecting these components. Communication buses 230 optionallyinclude circuitry (e.g., a chipset) that interconnects and controlscommunications between system components. Server 202 also includes powersource 224 and controller 212 coupled to mass storage 214. System 100optionally includes a user interface 232 comprising a display device 226and a keyboard 228. In other alternative embodiments, server 202 mayinclude alternate combinations of the elements described above, or mayinclude additional elements not described here.

Memory 236 may be high-speed random access memory, such as dynamicrandom-access memory (DRAM), static random-access memory (SRAM), doubledata rate random-access memory (DDR RAM) or other random access solidstate memory devices; and may include non-volatile memory, such as oneor more magnetic disk storage devices, optical disk storage devices,flash memory devices, or other non-volatile solid state storage devices.Memory 236 may optionally include one or more storage devices remotelylocated from the CPU(s) 222. Memory 236, or alternately the non-volatilememory device(s) within memory 236, comprises a non-transitory computerreadable storage medium. In some embodiments, memory 236, or thecomputer readable storage medium of memory 236, stores the followingprograms, modules and data structures, or a subset thereof: an operatingsystem 240; a file system 242; a network communications module 244; anda garment matching module 246.

Garment matching module 246 may be implemented as part of server 202 toimplement certain embodiments for garment matching as described herein.In other embodiments, such a garment matching module may be implementedon multiple devices, and may be implemented in a server-onlyarchitecture with a server such as server 202 presenting an interface toa user on a client device 10, in a client-server architecture withaspects of a garment matching module operating partially on a clientdevice 10 and partially on a server such as server 202, or as a clientapplication with the entirety of a system operating on a client devicesuch as client device 10-1 with no associated server 202. Additionaldetails related to certain embodiments of a garment matching module 246are described below.

The operating system 240 can include procedures for handling variousbasic system services and for performing hardware dependent tasks. Thefile system 242 can store and organize various files utilized by variousprograms. The network communications module 244 can communicate withclient devices (e.g., client device 10-1, client device 10-2, clientdevice 10-3) via the one or more communications interfaces 220 (e.g.,wired, wireless), the network 34, other wide area networks, local areanetworks, metropolitan area networks, and so on. In certain embodimentswhere cross border transactions involve multiple languages, acommunication module within memory 236 may function to automaticallytranslate information associated with a target garment that is in asecond language into a first language that is associated with a client.This may enable a client that is familiar with a first language to shopand select an appropriate size even when the size information ispresented in a second language that the client does not understand.

The network 34 may be any network that enables communication between oramong machines, databases, and devices (e.g., the server 202 and theclient device 10-1). Accordingly, the network 34 may be a wired network,a wireless network (e.g., a mobile or cellular network), or any suitablecombination thereof. The network 34 may include one or more portionsthat constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof. Accordingly, the network34 may include one or more portions that incorporate a local areanetwork (LAN), a wide area network (WAN), the Internet, a mobiletelephone network (e.g., a cellular network), a wired telephone network(e.g., a plain old telephone system (POTS) network), a wireless datanetwork (e.g., Wi-Fi network or WiMAX network), or any suitablecombination thereof. Any one or more portions of the network 34 maycommunicate information via a transmission medium. As used herein,“transmission medium” refers to any intangible (e.g., transitory) mediumthat is capable of communicating (e.g., transmitting) instructions forexecution by a machine (e.g., by one or more processors of such amachine), and includes digital or analog communication signals or otherintangible media to facilitate communication of such software.

The server 202 and the client devices (e.g., client device 10-1, clientdevice 10-2, client device 10-3) may each be implemented in whole or inpart by a computer system. A particular embodiment of one possibleimplementation of such a computer system is described below with respectto FIG. 10 .

Any of the machines, databases, or devices shown in FIG. 1 may beimplemented in a computer operating with a processor executingstandardized instruction sets and modified (e.g., configured orprogrammed) by software (e.g., one or more software modules) to be aspecial-purpose computer to perform one or more of the functionsdescribed herein for that machine, database, or device. For example, acomputer system able to implement any one or more of the methodologiesdescribed herein is discussed below with respect to FIG. 10 . Such aspecial-purpose computer may operate any number of modules using one ormore processors to implement various embodiments described herein forgarment size matching in a cross-border transaction.

As used herein, a “database” is a data storage resource and may storedata structured as a text file, a table, a spreadsheet, a relationaldatabase (e.g., an object-relational database), a triple store, ahierarchical data store, or any suitable combination thereof. Moreover,any two or more of the machines, databases, or devices illustrated inFIG. 1 may be combined into a single machine, and the functionsdescribed herein for any single machine, database, or device may besubdivided among multiple machines, databases, or devices.

Although FIG. 1 shows a system 100, FIG. 1 is intended more as afunctional description of the various features which may be present in aset of servers than as a structural schematic of the embodimentsdescribed herein. In practice, and as recognized by those of ordinaryskill in the art, items shown separately could be combined and someitems could be separated. For example, some items shown separately inFIG. 1 could be implemented on single servers and single items could beimplemented by one or more servers.

FIG. 2 further describes the exemplary memory 236 in server 202, asinitially described in FIG. 1 . FIG. 2 includes an expanded depiction ofexemplary file system 242. File system 242 may include any number ofdata structures and systems, including a garment database 250, clientsize information 260, and target garment information 270.

As shown in FIG. 2 , client size information 260 includes a clientpurchase history 261, client garment photos 263, and other client sizeinformation 263. Client size information 260 may include multiple typesof information, including any combination of client purchase history261, client garment photos 263, or other client size information 265. Incertain embodiments, client size information 260 may comprisephotographs of a client in well-fitting clothing. Client sizeinformation 260 may also comprise information regarding anotherindividual other than the user that the user has identified as theirsize proxy. Such client size information 260 may include any informationabout the size proxy such as size information for well-fitting clothingbelonging to the size proxy, measurements associated with the sizeproxy, or any other such information. Identifying a size proxy may alsosimply involve associating another client who size information is heldby the system with the current client. A size proxy may be a personknown to the client, a model provided by a system, or any other suchperson that may have measurements similar to a client's measurements.Client size information 260 may be text describing client body sizemeasurements, measurements associated with a client's favorite garments,measurements associated with a history of client purchases and returns,or other such text information that may be used to generate a base sizefor a client. A base client size as described herein refers to a systemdetermined value or set of values that is used to represent an estimateof a preferred or ideal size for a client. This base client size maythen be mapped to a standardized size scale along with the availablesizes of a target garment in order to create a recommendation. Incertain embodiments, a base client size may be a three dimensional modelof a garment. In other embodiments, a base client size may be a set oflinear measurements associated with a client body shape. In otherembodiments a base client size may be a value representing a number ofother measurements, where the value has been standardized as part of abrand's size scale, or a set of regionally standardized measurementsassociated with ready-to-wear clothes offered in the area.

In addition to including text descriptions, client size information 260may also be images of one or more garments that may be analyzed andmodeled to determine a base client size. In other embodiments, any datarelevant to determining a base client size may be included in clientsize information 260. Client size information 260 may be received from aclient device 10 and stored temporarily within file system 242, or maybe received and then stored permanently as part of a garment database250. In certain embodiments, client size information 260 may be receivedas part of a size matching processes requested by a particular client,with client size information 260 stored in file system 242 for useduring the client's size matching process. Identifying details from theclient size information 260 may then be removed, and the details ofclient size information 260 stored anonymously in user purchasehistories 251. This information may then be used for later size matchingprocesses by other clients.

Client purchase history 261 may refer to information input by a clientinto a user interface presented at a client device such as client 10 ofFIG. 1 . Client purchase history 261 may alternatively be a purchasehistory that is accessed by a third party device, or by a moduleoperating on a client device that records a history of client purchasesand returns. The history of client purchases and returns, including sizedetails, brand details, or other information related to the size andshape of purchased and returned garments previously involved intransactions with a client may be sent directly as part of client sizeinformation 260, or may be filtered to send particular details by aclient module. In certain embodiments, such a client module may operateas part of a service to provide size matching, and may involve a userregistration, where a user provides registration details to a web serverassociated with server 202 and the client module, and agrees to havepurchase information gathered and stored as part of service operation.In certain embodiments, such a service may be provided on a stand-alonebasis independent of any of the client purchases. In other embodiments,an Internet sales portal may provide such a service in conjunction withsales made to the client. In such a system, the collection and storageof data may be performed entirely by one or more servers associated withtransactions involving the client and the service provider.

In certain embodiments, client size information 260 may comprise imagesof a garment, part of a garment, or multiple garments as client garmentphotos 263 that may be analyzed to derive a base client size. Additionaldetails related to use of such images are described below, particularlywith respect to FIGS. 7-9 . In other embodiments, client purchasehistory 261, client garment photos 263, and other client sizeinformation 265 may all be used as part of a single process to generatea size map and recommend a size to a user.

In addition to the file system comprising client size information 260,the system may also store target garment information 270. Such targetgarment information 270 may comprise information about a particulargarment design, a particular style, or a general client preference. Incertain embodiments, this may include general search terms, one or moreimages of a garment that a client is searching for or wishes to receivea size recommendation for, style and shape preferences associated with aclient including example brands, sizes, shapes, and styles, or any othersuch information related to a user search for one or more garments. Thisinformation may be received from a client or client device as part of asize match request, or this information may be stored in a clientprofile as part of a service provided to a client by a server 202. Incertain embodiments, target garment information may identify a web pageor sales portal associated with a specific garment design. This targetgarment information may then be used identify details related to thegarment in a garment database 250. Such details may include informationfrom the web page that may be placed into garment database 250 as partof file system 242, and may identify a plurality of sizes available inthe specific garment design.

Garment database may 250 may include user purchase histories 251,garment CAD documents 252, garment models 253, and aggregated data 254.User purchase histories 251 may include information gathered from clientpurchase history 261 instances, which may be placed in garment database250 with information from any number of different clients. Thisinformation may be used to generate size, shape, and style matching dataas aggregated data 254. For example, if a particular group of clientsall order size 1 of brand A, and size 5 of brand B, this information maybe used, at least in part, to map size 1 of brand A to size 5 of brandB. Similar groups of clients ordering other sizes of both brand A andbrand B may create information about a plurality of sizes for aparticular brand. Similar mappings may be created for countries,regions, particular designs, brands and other groupings relevant to agarment database.

Garment database may also include garment CAD files 252. These files mayinclude measurements and design details related to the expected size andconstruction of various sizes of a particular garment design. A CAD file252 may also include details of materials used in a garment, andproperties of the materials such as stretching characteristics,malleability, bendability, thickness, or other such material properties.This information may be gathered from a designer or created by ananalysis of garments. This information may then be used in conjunctionwith other information about a garment to create a mapping andrecommendation for a client based on client preferences.

Garment models 253 as described herein refer to simulations and/orcomputer structures which describe a garment, a garment style, a garmentbrand, and/or other such garment information. Such garment models 253may be generated in a variety of ways, including custom models createdby a system administrator. In certain embodiments, garment models 253may be generated automatically from images. These images may be receivedas part of client size information from one or more users, or may begenerated by an operator of a server 202. Additional details related togeneration of such models is described below with respect to FIGS. 7-9 .

Aggregated data 254 may take any information from user purchasehistories 251, garment CAD files 252, garment models 253, and generatepreprocessed information about one or more garment designs, styles,shapes, or associations between any of these and a particular area,country, or client preferences. As a system operates and additionalclient size information 260 is received, this information may beautomatically integrated into garment database 250 and used to updateaggregated data based on input information. Additionally, if a usermakes a purchase based on a system recommendation or does not make apurchase following a recommendation, this information may also be usedas feedback into garment database 250 to update models, associations,and expected preferences associated with certain garments, garmenttypes, or garment styles.

FIG. 3 is a block diagram illustrating components of the garmentmatching module 246, according to some example embodiments, as initiallydescribed in FIG. 1 . The garment matching module 246 is shown asincluding a size match request gathering module 381, a client sizeinformation analysis module 382, a target garment analysis module 383, agarment database analysis module 384, a size mapping module 385, a sizesimulation module 386, an interface module 387, and a languagetranslation and communication module 388.

Size match request gathering module 382 may operate to coordinate andreceive size match requests from client devices 10 received via network34, communications interface 220, and network communications module 244.It may additionally parse text size information such as client purchasehistory 261 information, and may identify client garment photos 263 tobe stored as client size information 260 which is received as part of asize match request from a particular client. Size match requestgathering module 382 may also identify target garment information 270which is received as part of a size match request, and may direct theinformation to be stored in the correct portion of file system 242 andto be processed by the appropriate process of garment matching module246.

Once client size match request gathering module 381 has processed anincoming size match request, client size information analysis module 382may process the client size information from a size match request toidentify a base client size. This may be done by simply parsing textsize information provided by a client to a standardized size scale, byanalyzing garment details associated with the client to identify thebase client size, or by generating a garment model and then deriving abase client size from the garment model.

Target garment analysis module 383 analyzes target garment informationto identify one or more target garments to be matched with a base clientsize. As part of this process, target garment analysis module 383 mayinteract with garment database analysis module 384 to identify garmentsfrom garment database 250 which most closely match the target garmentinformation from a size match request. For example, if the targetgarment information identifies a particular garment style, then targetgarment analysis module 383 may use garment database analysis module 384to identify a set of target sizes associated with the particular garmentstyle using information from garment database 250. If, on the otherhand, the target garment information describes client shape and stylepreferences without identifying a particular garment style, the targetgarment analysis module may identify measurement characteristicsassociated with segments of a garment, and pass these characteristics tothe garment database analysis module 384 to identify garments that arewithin a threshold of matching such characteristics. The thresholdvalues may be input by a system administrator to match predeterminedshape and style expectations. In other embodiments, the threshold valuesmay be based on an analysis of previous responses to clientrecommendations stored in user purchase histories 251 and aggregateddata 254. The thresholds may then be based at least in part on userpurchases and returns made following a system mapping andrecommendation.

If the client size information, the target garment information, orgarment database images are involved in an analysis to determine a baseclient size or a target garment, size simulation module 386 may be usedto analyze the images and create a garment model 253. Details of suchgarment model creation are described below with respect to FIGS. 7-9 .Once a garment model 253 is created by size simulation module 386, itmay be analyzed to identify one or more particular sizes associated withthe modeled garment, and may be used as part of a mapping process.

Once a base client size, one or more target garments, and a set oftarget garment sizes have been identified, size mapping module 385 maythen determine a standardized size scale which may be used as a basisfor mapping a base client size and a set of target garment sizes. Such astandardized size scale may be based on linear measurements of aspectsof a garment, on a complex three dimensional model of each size of eachgarment and a base client size, or on any other such scale. The sizescale may also incorporate information about formation properties of thegarments such as stretch or other properties such as thermal behaviorwhich may be used for comparison between garments or materials. Once thebase client size and the set of target garment sizes have been placed onthe standardized size scale, a comparison may be done to determine whichtarget garment size of the set of target garment sizes is closest to thebase client size.

If the standardized size scale is based on linear measurements of partsof a garment or user size, then the closest size of the set of targetgarment sizes may be the size which has the least linear differencevalue with the base client size. In other embodiments, certainmeasurements may be weighted more heavily, or the closest match may bethe size which has the closest match without having any dimension thatis smaller than a base client size dimension. In other embodiments, athreshold match difference may be set, such that if no target garmentsize of the set of target garment sizes meets the threshold criteria,then the system will not provide a size recommendation, or willrecommend against purchase of a garment design.

As part of system operation, an interface module 387 may be used topresent an interface to both system operators and to clients using asystem. In certain embodiments, a user interface from interface module387 may be communicated to a client device 10 to accept a size matchrequest from a client. In other embodiments, a user interface frominterface module 387 may be presented to a system operator to selectthreshold settings associated with system operation. Similarly, languagetranslation and communication module 388 may manage communications witha client, and may operate to translate size and descriptive materialswhen target garment details and sizes are available in a languagedifferent that the language of a client. For example, an interfacemodule 387 may receive a language preference input from a clientindicating that the client does not understand French, but wishes torequest a size match for a garment from a website that is presented inFrench, and to receive the results in Mandarin. A size match request tothe system from a client device may provide details for generating abase client size, and may identify a website for a garment that ispresented in French. The system may receive the information, identifythe base client size and a plurality of target garment sizes, and mapthe base client size to the set of target garment sizes. The system maythen generate a size recommendation communication in Mandarin, andconvey that message to a client device. The communication in Mandarinmay include not only size match recommendation, but may includeinstructions for how to select the appropriate size on a website that ispresented in French. The instructions may thus enable the client toorder the recommended garment as part of a cross border transaction in alanguage that the client does not understand. In other embodiments,language translation and communication module 388 may automaticallytranslate the information from the web page, and act as an intermediarynot only to recommend the correct size in a language that the clientunderstands, but also to implement the transaction on behalf of theclient by communicating with website on behalf of the client. This mayenable a client to select an input on an interface module 387 to accepta system recommendation, and to initiate an order for the garment in therecommended size using the system as an intermediary.

Any one or more of the modules described herein may be implemented usinghardware (e.g., one or more processors of a machine) or a combination ofhardware and software. For example, any module described herein mayconfigure a processor (e.g., among one or more processors of a machine)to perform the operations described herein for that module. Moreover,any two or more of these modules may be combined into a single module,and the functions described herein for a single module may be subdividedamong multiple modules. Furthermore, according to various exampleembodiments, modules described herein as being implemented within asingle machine, database, or device may be distributed across multiplemachines, databases, or devices.

Each of the above identified elements may be stored in one or more ofthe previously mentioned memory devices, and corresponds to a set ofinstructions for performing a function described above. The aboveidentified modules or programs (i.e., sets of instructions) need not beimplemented as separate software programs, procedures or modules, andthus various subsets of these modules may be combined or otherwiserearranged in various embodiments. In some embodiments, memory 236 maystore a subset of the modules and data structures identified above.Furthermore, memory 236 may store additional modules and data structuresnot described above.

The actual number of servers used to implement a size matching module246 and how features are allocated among them will vary from oneimplementation to another, and may depend in part on the amount of datatraffic that the system handles during peak usage periods as well asduring average usage periods.

FIGS. 4-6 are flowcharts representing different methods of size mappingaccording to various embodiments. In certain embodiments, the methodsare governed by instructions stored in a computer readable storagemedium and that are executed by one or more processors of one or moreservers. Each of the operations shown in FIGS. 4-6 may correspond toinstructions stored in a computer memory or computer readable storagemedium.

Operations in the various methods 400, 500, and 600 of FIGS. 4-6 may beperformed by the server 202, using modules described above with respectto FIG. 3 . The computer readable storage medium may include a magneticor optical disk storage device, solid state storage devices such asflash memory, or other non-volatile memory device or devices. Thecomputer readable instructions stored on the computer readable storagemedium are in source code, assembly language code, object code, or otherinstruction format that is interpreted by one or more processors.

The foregoing description, for purposes of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the present disclosure to the precise forms disclosed. Manymodifications and variations are possible in view of the aboveteachings. The embodiments were chosen and described in order to bestexplain the principles of the present disclosure and its practicalapplications, to thereby enable others skilled in the art to bestutilize the present disclosure and various embodiments with variousmodifications as are suited to the particular use contemplated.

Method 400 of FIG. 4 begins at element 410 with a system receiving, froma first user system, a first size match request associated with a crossborder transaction, wherein the first match size match request comprisesclient size information and target garment information. Such informationmay be processed by a garment matching module 246, with information fromthe first size match request stored in a file system 242. In certainembodiments, a system may be receiving size match requests from multipleusers, such that second, third, and any number of other size matchrequests may be received and processed while the system is responding tothe first size match request as described by method 400.

Method 400 continues in element 420 with identifying the client sizeinformation and analyzing the client size information to determine abase client size. This identification and determination may be performedby a client size information analysis module 382 as described above, orby any other such analysis process.

Element 430 then involves identifying the target garment information andanalyzing a garment database to identify a set of target garment sizes.This identification may be performed by a module such as target garmentanalysis module 383 in conjunction with a garment database analysismodule, or by any other such module or group of modules. In variousembodiments, this may involve the use of a size simulation module 386 ifany size determination is based on a model. In certain embodiments, thesize determinations may be made without the use of a model, and may bebased on size information received directly from a client or a garmentdatabase.

Element 440 then involves analyzing the target garment information withthe base client size to generate a map of the base client size to theset of target garment sizes. Such an analysis may be performed by amodule such as size mapping module 385.

Element 450 then involves generating a first size match communicationbased on the map of the base client size to the set of target garmentsizes, the first size match communication identifying a closest matchbetween the client size information and a first target size of the setof target garment sizes and communicating to the first user system, afirst size match communication.

FIG. 5 describes an alternate method for generating a map and a garmentmatch when a size match request explicitly includes informationidentifying the cross-border nature of the transaction along with aparticular garment design. Such a method may occur, in one embodiment,where a client accesses a web page offering to sell a garment from adifferent region than where the client is located. The size matchingmethod may be used when sizing conventions or descriptions are presentedby the web page in a format, size scale, or language that is notunderstandable to the client.

The embodiment of FIG. 5 shown by method 500 may begin in element 510with a system receiving, from a client device in a first country, afirst size match requests which includes target garment informationidentifying a particular garment design associated with a secondcountry. The country associations may be based on a location of thecomputing devices, a language of a user interface associated with thecomputing devices, a system input or analysis received from a client anda query to a web site, or any other such association. The particulargarment design may be a design number, sales number, an image of agarment, or any other such identifying information. The particulargarment design may further be associated with a set of target garmentsizes. For example, the web page may indicate that the garment isavailable in small, medium, and large sizes. Such information may not beassociated with standardized measurements, sizes, or shapes that aremeaningful to the client located in a different country. A garmentdatabase of the system, however, may have additional information orrecords that may be used to generate such a standardized size mappingfor the client.

Element 520 then involves determining a base client size. This may bedone in one or more of three different ways. The base client size may bedetermined using a history of one or more garment purchases associatedwith the client. This information may have been previously gathered bythe matching system, or may be provided by the client along with thesize match request. The base client size may be determined by analyzinga computer aided design file associated with a client identifiedgarment. For example, the size match request may identify a brand andgarment that is a favorite garment of the client, along with a garmentsize. The matching system may access a garment database to retrieve aCAD file associated with the garment, and may then analyze the CAD fileto determine the base client size. In other embodiments, the matchingsystem may access such a CAD file from a third party database, such as abrand manufacturer website, or the CAD file may be provided by theclient. Alternatively, the client may provide one or more images of agarment associated with the client, and the matching system may analyzethe files to generate a garment model. The garment model may then beused to determine a base client size, since the client has identifiedthe garment as being associated with a preferred or ideal size for theclient. In different embodiments, all of these methods may be used, andthe results averaged or weighted to determine the final base clientsize.

Element 530 then involves a similar process for determining a set oftarget garment sizes. Since the particular garment design is identifiedby the size match request, the system does not need to search forgarments with similar shapes and styles, but merely needs to identifyhow the available sizes for the particular garment design will relate tothe determined base client size. In certain embodiments, specificdetails related to a set of target garment sizes may already beavailable in a garment database. If not, the system may determine theset of target garment sizes in one or more of the following ways. Thematching system may analyze a history of size purchases associated withthe particular garment design made by other clients. If client historyinformation is able to link purchases of both the target garment in aparticular size to purchases of a garment similar to one detailed in thesize match request from the client, then the system may be able toassociate the sizes, even in specific measurements of exact shape andsize details are not available. Also, similar to what is described abovefor the base client size, a CAD file may be identified and analyzed, ora model may be generated from images of the particular garment design.

In element 540, the base client size and the set of target garment sizesare mapped to a standardized size scale. While in certain embodiments,the standardized size scale may involve particular geometricmeasurements, in other embodiments, the standardized size scale may beinterpolated or suggested based on a history of purchases and returnsfrom other clients and system users. In such embodiments, exactgeometric models and measurements may not be available for the mapping,and so a relative map may be estimated based on purchases and returns.For example, a second client known to have a same size and body shape asthe client may have purchased and returned the particular garments in agiven size. This may be used by the system to map that size away fromthe correct size for the client that initiated the size match request.If a different size was then ordered and not returned, this may be usedto map the second not returned size to the size of the client thatrequested the size match request. The more such data that is in thesystem, the stronger the confidence in the mapping may be. A size matchcommunication may therefore include a confidence indicator as part ofthe communication based on the map of the base client size to the set oftarget garment sizes.

In certain embodiments, a matching system may not provide arecommendation, but may simply present a mapping with a base client sizeand the set of target garment sizes on the map. This information maythen be used by the client to make their own decision regarding whichsize to order or whether to order the garment at all.

FIG. 6 then describes an alternative embodiment as method 600. Method600 describes an embodiment where a particular target garment item ortarget garment type is not provided by the client. Instead, the systemuses a set of client style or shape preferences to search for garmentdesigns, and presents a mapping of garment designs to the client.

Method 600 begins with element 610 receiving a first size match requestwhich includes a set of shape and/or style preferences from the client.The size match request also identifies a target region without identifya particular target garment item or target garment type. For example, atourist from the United States of America visiting Eastern Europe mayidentify a region including multiple countries for garment matchingalong with a set of shape and style preferences. Similarly, a Europeantourist visiting the west coast of the United States may identify a cityor particular portion of the country for a size match.

Element 620 involves analyzing client size information from the firstsize match request to identify a base client size as described above.

Element 630 involves analyzing a garment database to identify aplurality of garments having a threshold similarity to the set of shapeand/or style preferences. This analysis may be based on any combinationof client history information, CAD files, or garment models describedherein. The threshold values may be based on system parameters, onclient selections, or on a combination of client preferences and systemparameters. In certain embodiments, for example, a history of returninformation associated with similar searches may be used to determinesystem confidence values in a recommendation or match, which may be usedto adjust different thresholds of size and shape matching. Additionalembodiments may also include color and pattern matching associated withsuch searches. Such size and shape preferences may be based not only onuser inputs, but may also be based on garments identified by a client.For example, a client may provide images of 5 different garments with asize match request to identify brands or garments from the target regionwhich match the garments from the images. The system may then analyzethe garment database to retrieve additional information about thegarments from the images, and then further search the garment databasefor garments originating from the target region which share size andshape similarities.

In certain embodiments, a client may request only a partial match. Forexample, a user may provide images of pants, with a request only tomatch the size and shape of the bottom of the pant leg. Alternatively, aclient may provide images of collared shirts, with a request to onlymatch the collar shape and style.

One a set of sufficiently matching garments are identified in element630, the system may then optionally further analyze the available sizesfor the matched garments. A mapping of the target garment sizes to thebase client size may then be provided. In alternate embodiments, thematching system may provide a map of style similarities withoutproviding a mapping of size similarities. For example, if a particularcollar style is requested, a map of the similarities and differencesbetween the identified target garment collars and the identified collarstyle may be made.

In element 650, the first size match communication is generated. Thematch system communicates match information related the requested shapeor style information as mapped to the plurality of garments having thethreshold similarity to the set of shape or style preferences. Incertain embodiments, this may be a list of garments, with a match valueidentifying how closely each garment matches the requested preferences.This may be, for example, a percentage based match based on a linear orweighted analyses of the physical dimensions of the preference and thegarment. This may be a scored non-linear scale based on historyinformation associated with previous purchases. In other embodiments,this may include multiple types of matching scores or information, alongwith confidence scores relating to the strength or volume of the datathat the match is based on.

As described above, certain embodiments include processes foridentifying a match between a base client size and one of a plurality oftarget garment sizes. In certain embodiments, models may be used for abase client size, with boundary detection algorithms used to detect theclosest match with one of the sizes of the plurality of target garmentsizes. For example, a base client size may include a set of minimum sizeboundaries, and the closest match may be considered the target garmentsize from the plurality of target garment sizes that is larger than allminimum size boundaries of a base client size. In other embodiments,other matching algorithms may be used to match a base client size to atarget garment size.

Embodiments described above in methods 400, 500, and 600 may be modifiedin any number of ways. Certain embodiments may include elementsprocessed or performed in different order, in different combinationswith repeated elements, or with multiple methods performed together aspart of a single process. For example, an additional embodiment maycomprise receiving the first user system, a second size match request,wherein the second size match request is associated with the client sizeinformation second target garment information. The system may verify thebase client size and identify additional target garment informationbased on the second target garment information and a user response to afirst size match communication identifying a closest match between theclient size information and a first target size of the set of targetgarment sizes. The system may thus incorporate feedback from a client'spurchases and returns to improve matching. Further, similar feedbackfrom other client's purchases and returns may be used, such that a thirdsize match request may be processed from a second client, and the secondclient size information and second client target garment information maybe used by the system as part of a garment database to improve matchingfor subsequent size match requests from both the first client, thesecond client, and other additional different clients.

In various embodiments, size match requests may be particularlyassociated with a cross border transaction, wherein the client sizeinformation is associated with a first country, and wherein the targetgarment information is associated with a second country which isdifferent than the first country. Other size match requests may identifyregions or language areas with sizing and translation issues similar tothose described for cross border transactions between countries.

In certain embodiments, system matching processes may involve analyzingthe client size information to determine the base client size comprisesanalyzing garment sizes associated with the history of one or moregarment purchases associated with the client and analyzing returnedgarment sizes associated with the history of one or more garmentpurchases to identify a preferred size associated with the history ofone or more garment purchases and setting the preferred size as the baseclient size. In other embodiments, this information related to apreferred size may be used as one variable in setting a base clientsize.

In certain embodiments, as part of mapping cross border transactions orinstead of mapping cross border transaction sizing, the mapping may beassociated with particular brands instead of particular garments. Thismay include identifying a plurality of brands associated with the one ormore garment purchases, mapping brand sizes for the plurality of brandsto a standardized size scale, and identifying the preferred size usingthe standardized size scale.

Additionally, boundary detection algorithms may also be used as part ofprocesses for generating garment models from images for use in bothidentifying a base client size and identifying one or more targetgarments.

One example of a boundary detection algorithm can be to determine thecolor-range of the background of the image by averaging out pixel valuesat the boundary (e.g., first row, first column, last row, last column)of the input image. The background color can be determined to be B(i.e., B_(RED), B_(GREEN), B_(BLUE)). Additionally, a pre-determinedthreshold value (t) can be chosen. The threshold value can be set by theuser or calculated by the system (e.g., system 100). All pixel values inthe received images that are within a range of the background color(i.e., B_(RED)+/−t, B_(GREEN)+/−t, B_(BLUE)+/−t) are interpreted asbackground pixels, and hence not part of the garment. Having identifiedeach pixel value as either foreground (i.e., part of garment) orbackground, for each row of pixels, the pixel values where there is atransition between foreground and background can be identified as thecontour/garment boundary pixels. Using these boundary pixels, an outlinecan be used to generate a partial shape of the garment.

In another example of a boundary detection algorithm, for each row ofpixels, the intensity (or color value) at each pixel is compared to theintensity (or color value) of the previous pixel. For a pre-determinedthreshold, once the difference between consecutive pixel values exceedsthe threshold, the identified pixels can be classified as boundarypixels. In some instances, the intensity values for the foreground andbackground can be assigned via the scan line method. The scan linemethod includes traversing individual pixels and assigning thedesignation of background to the colors that match the outer edges ofthe photograph. In another instances, the boundary can be identified(e.g., extracted) using a gradient calculation method. In the gradientcalculation method, differences in pixel color and intensity arecalculated between adjacent pixels. A boundary can be identified whenthe differences are above a predetermined threshold value (e.g., sharpdifference in pixel color and/or intensity between adjacent pixels). Inyet other instances, the boundary can be determined using both the scanline method and the gradient calculation method. Using both methods canallow for a more accurate identification of the boundary.

Generating the partial shapes can include creating a continuous curveusing the identified boundary. As mentioned, the identified boundary canbe a discrete set of points. The discrete set of points can be a set ofvertices associated with pixels that have been identified as boundarypoints using a boundary detection algorithm. The curve can be created byjoining the discrete set of points that are determined to be boundariesof the garment and then running a smoothing function to eliminateoutliers. Additionally, the curve can be modified based on a garmenttemplate from the garment database. The curve can be smoothed out byeliminating noise (e.g., remove outliers from the data). For example,noise can refer to the artifacts in image acquisition (e.g., lighting,image compression). Hence, the process of noise removal can help createa smooth edge instead of a jagged edge.

Moreover, the precision can be adjusted to accommodate varying levels ofdesired accuracy of the created digital garment and can be based oncomputation power. The precision can be automatically adjusted by thesystem based on the client device (e.g., lower precision for mobiledevice, higher precision for large screen display). In some instances,the standard error of tolerance is a parameter that can be set.Tolerance can be measured by actual units of distance (e.g., 0.01inches). Alternatively, tolerance can be measured in number of pixels.

Furthermore, accuracy parameters can be received (e.g., from a user) ordetermined to help identify the boundary of the garment or a base clientsize. Accuracy parameters can include, but are not limited to, extractedgeometry files, extracted texture files, stitching information files andgarment templates as part of a garment database 250.

Optionally, texture and optical properties can be determined from theimages (e.g., photographs) as stored in the extracted texture files. Thetexture information can be used to determine the material properties ofthe garment and can be used to generate the texture map as part of agarment model. The material properties of the garment can be used forcalculating the simulated forces as part of a garment map. Furthermore,the material properties can be matched to the garment template in agarment database in order to determine the type of garment using atexture mapping module that may be part of a size simulation module forcreating garment models 253. For example, the system can identify pleatsin a garment when every part of the garment is captured in one of theinput images. Moreover, the material property can be extracted even ifthe images of the garment are stretched or sheared. The opticalproperties can be used during the optional operations of applying atexture map to a garment model. A garment associated with images maythen be used to determine a type of garment by comparing the generatedfirst and second partial shapes to a database of reference garmentshapes using the garment database.

FIGS. 7-9 illustrate aspects of garment models and garment modelinformation that may be stored in file system 242 as part of garmentmodels 253. For example, in FIG. 7 , the jeans garment template 705 caninclude information such as the number of panels 710, stitchinginformation 715 of the jeans, body placement parameters 720 of thejeans, draping parameters 725, simulation parameters 730, and otherrelevant information associated with the jeans garment template. Variousparameters may include location parameters within the context of aphysical description of aspects of a garment, described within acoordinate system. Parameters may also describe modeled physicalcharacteristics such as elasticity, folding or bending descriptions,fabric resilience and thickness, or other such descriptors of a garmentor elements of a garment.

In another example, in FIG. 9 , the sleeveless dress garment templatecan include information such as the number of panels, stitchinginformation of the dress, body placement parameters of the dress,draping parameters, simulation parameters, and other relevantinformation associated with the sleeveless dress garment template. Thisinformation may then be used to create garment models, and to searchexisting garment models using client size and shape preferences.

For example, as illustrated in FIG. 8 , two images (e.g., photographs)of the front of the jeans and the back of the jeans can be sufficientwhen all parts of the garment are captured in the images. Using the twoimages, the size simulation module 386 can generate a first partialshape corresponding to the front of the jeans 810 and a second partialshape corresponding to the back of the jeans. Then, the garment modelcreation process may determine that the received images are images of apair of jeans by comparing the generated partial shapes to a jeansgarment template the garment database 250. Moreover, based on thedetermination that the garment is a pair of jeans, the system can jointhe partial shapes to generate a 3-D pair of the digital jeans as partof a garment model of the jeans.

In certain embodiments, a garment database can hold entries fordifferent garments (e.g., jeans garment template, sleeveless dressgarment template, blouse garment template, sweater garment template,shirt garment template). In some embodiments, if the shape does notmatch a previously stored entry in the basic garment database, thenalgorithms may be needed to provide guidance in sewing the sidestogether for the particular new garment shape. Alternatively, theintervention can be automated. The shape can then be stored as a newentry into the basic garment database. These templates may be searchedalong with particular garment models to simplify various elements of thegarment size matching processes described herein.

As part of garment modeling, certain embodiments may modify a generated3-D garment model by adding a second group of vertices to the generated3-D garment model using the tessellation. Tessellation can includebreaking down (e.g., tiling) a garment into many tessellated geometricshape to generate a tessellated garment model. For example, the shirtcan be tessellated with triangles (e.g., about 20,000 triangles whentriangle edge is around 1 centimeters), and the vertices of thetriangles can be the second group of vertices in the generated 3-Dgarment model. The vertices of the triangles can give locationinformation of certain points in the material. The location informationcan be an x, y and z position value, and the location position can beindependent of color and design of the garment.

Tessellation can be used to determine the location of certain points inthe material of the garment. The certain points in the material of thegarment can be represented by planar shapes. For example, the interiorof the boundary of the garment can be filled with a plurality of similargeometric shapes. The points used for the tessellation can be based onthe vertices of the shape. The shapes for the tessellation can betriangles, given that triangles are an efficient way (e.g., lesscomputational power, faster tessellation speed) of representing atessellated garment.

Furthermore, the points of the tessellated geometric shape can bendoutside the shape, but not within. For example, if the tessellated shapeis a triangle, different triangles can be folded over other triangles,but a triangle cannot be folded within itself. In other words, thetriangle itself remains planar. In such example, the three vertices ofthe triangle determine the three points. An example tessellation can bean extracted shape (e.g., a shirt shape) being filled with a pluralityof triangles, each with edges that can be calibrated (e.g., 1 cm). Thus,each point on the shirt can be approximated or located by reference tothe nearest vertex on the most proximate triangle to the location of thedetermined position. In some embodiments, the triangles are equilateraltriangles to maximize efficiency. In some arrangements, tessellation isconsistent for each garment and thus, in the example, the same 1 cm edgetriangle shape is used for tessellation of all extracted shapes.Alternatively, different tessellation shapes are used for differentextracted shapes. Furthermore, tessellation can refer to the location ofpoints of material and can be independent of the color and design of thegarment.

In various embodiments, data of tessellation and boundary can becompatible with single instruction multiple data (SIMD). SIMD can be atype of vector processor that uses the same instruction on multipleelements. SIMD compatibility can ensure that the code is consistent withthe hardware. Making the processes SIMD friendly can allow forutilization of the hardware in a more efficient manner because currenthardware includes processors or processors with SIMD units.Additionally, the tessellation can be done in parallel (e.g., performingthe tessellation using multiple SIMD units in parallel) in order toincrease the tessellation speed, and the simulation of the garment underdifferent scenarios.

Optionally, certain embodiments can include an operation forcalibration. In such embodiments, first and/or second images of agarment or portions of a garment can include an object (e.g., creditcard) with a known size for to calibrate the boundary and size of thegarment. In various embodiments, identifying the boundary can includecomputing shape and size of the garment. In some instances, thecalibration object can be placed on the garment, where the calibrationobject is clearly visible in the photograph but not distinct from thegarment itself. A square object may be a better object for calibrationbecause of the straight lines, four equal sides and four equal angles.

Garment modeling can include generating multiple sizes of the samegarment by scaling or distorting the 3-D digital garment model. Scalingor distorting the 3-D digital garment model can generate 3-D models thatare representative of the family of sizes of a garment typically carriedand sold by retailers. Alternatively, scaling or distorting the 3-Ddigital garment model can generate a specific sized version of thegarment. The distortion of the 3-D digital garment model can be uniformfor the entire model (i.e., the entire model is grown or shrunk), orspecific to individual zones (e.g., specific garment areas) withdifferent distortions (e.g., scale factors) for the individual zones.Additionally, the scaling of dimensions of the garments can be arbitrary(as in the case of creating a custom size), or can be according tospecifications. The specifications can be based on grading rules, sizecharts, actual measurements, and/or digital measurements.

In certain embodiments, a base client size may be used to generate arepresentative geometry of a client, and a 3-D digital garment model formultiple sizes of the same garment design may be generated in order tomodel the fit of the garment as part of mapping a set of target garmentsizes to a base client size. This may involve use of a cloth engine cantake as input tessellation and material properties and can output 3-Dmodels of clothing on client avatars. The cloth engine can move thepoints around to fit a 3-D body model based on a simulated force (e.g.,friction, stitching force). Additionally, based on this modeling, thepoints are connected via spring models as part of the garment modelingand the spring models can be stretched based on a simulated force (e.g.,gravity, material property of garment). The cloth engine can solve asystem of equations, given that the equations are all inter-connected.In one example, the system of equations can be based on the spring forceon each vertex.

Thus, in certain embodiments, a garment model may be generated bygenerating a first partial shape of a garment based on the first imageand generating a second partial shape of the garment of the garmentbased on the second image. A type of garment may then be determined bycomparing the generated first and second partial shapes to a database ofreference garment shapes. The garment model may be initially generatedby joining the first partial shape and the second partial shape based onthe determined type of garment, the generated three-dimensional garmentmodel including a first group of vertices. The garment model may furtherbe adjusted by tessellating the generated three-dimensional garmentmodel by adding a second group of vertices to the generatedthree-dimensional garment model. In certain such embodiments where atriangle among the triangles of the tessellated three-dimensionalgarment model has a minimum angle, the model creation process may beoptimized to tessellate the generated three-dimensional garment model bymaximizing the minimum angle of the triangle

The model may then be used in a variety of fashions, includingdetermining the base client size from the generated three-dimensionalgarment model, performing a match between an avatar representing a baseclient size and a plurality of models for target garment sizes, or assearchable garment database information used to identify garmentsmeeting threshold size and shape requirements.

FIG. 10 is a block diagram illustrating components of a machine 1000,according to some example embodiments, able to read instructions 1024from a machine-readable medium 1022 (e.g., a non-transitorymachine-readable medium, a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 10 shows the machine 1000 in theexample form of a computer system (e.g., a computer) within which theinstructions 1024 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 1000 toperform any one or more of the methodologies discussed herein may beexecuted, in whole or in part. Server 202 can be an example of machine1000. In various embodiments, a machine such as machine 1000 may be usedto implement any computing device referred to herein, including clientdevices 10, server 202, networking devices that are part of network 34,and any other implementation of a distributed, virtual, or othercomputing structure which may be used to implement the embodimentsdescribed herein.

In alternative embodiments, the machine 1000 operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 1000 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a distributed (e.g., peer-to-peer)network environment. The machine 1000 may be a server computer, a clientcomputer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a cellular telephone, a smartphone, a set-top box(STB), a personal digital assistant (PDA), a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1024, sequentially or otherwise, that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executethe instructions 1024 to perform all or part of any one or more of themethodologies discussed herein.

The machine 1000 includes a processor 1002 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 1004, and a static memory 1006, which areconfigured to communicate with each other via a bus 1008. The processor1002 may contain microcircuits that are configurable, temporarily orpermanently, by some or all of the instructions 1024 such that theprocessor 1002 is configurable to perform any one or more of themethodologies described herein, in whole or in part. For example, a setof one or more microcircuits of the processor 1002 may be configurableto execute one or more modules (e.g., software modules) describedherein.

The machine 1000 may further include a graphics display 1010 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine1000 may also include an alphanumeric input device 1012 (e.g., akeyboard or keypad), a cursor control device 1014 (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, an eye trackingdevice, or other pointing instrument), a storage unit 1016, an audiogeneration device 1018 (e.g., a sound card, an amplifier, a speaker, aheadphone jack, or any suitable combination thereof), and a networkinterface device 1020.

The storage unit 1016 includes the machine-readable medium 1022 (e.g., atangible and non-transitory machine-readable storage medium) on whichare stored the instructions 1024 embodying any one or more of themethodologies or functions described herein. The instructions 1024 mayalso reside, completely or at least partially, within the main memory1004, within the processor 1002 (e.g., within the processor's cachememory), or both, before or during execution thereof by the machine1000. Accordingly, the main memory 1004 and the processor 1002 may beconsidered machine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 1024 may be transmitted orreceived over the network 34 via the network interface device 1020. Forexample, the network interface device 1020 may communicate theinstructions 1024 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 1000 may be a portablecomputing device, such as a smart phone or tablet computer, and have oneor more additional input components 1030 (e.g., sensors or gauges).Examples of such input components 1030 include an image input component(e.g., one or more cameras), an audio input component (e.g., amicrophone), a direction input component (e.g., a compass), a locationinput component (e.g., a global positioning system (GPS) receiver), anorientation component (e.g., a gyroscope), a motion detection component(e.g., one or more accelerometers), an altitude detection component(e.g., an altimeter), and a gas detection component (e.g., a gassensor). Inputs harvested by any one or more of these input componentsmay be accessible and available for use by any of the modules describedherein.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1022 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions 1024. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing the instructions 1024 for execution by the machine1000, such that the instructions 1024, when executed by one or moreprocessors of the machine 1000 (e.g., processor 1002), cause the machine1000 to perform any one or more of the methodologies described herein,in whole or in part. Accordingly, a “machine-readable medium” refers toa single storage apparatus or device, as well as cloud-based storagesystems or storage networks that include multiple storage apparatus ordevices. The term “machine-readable medium” shall accordingly be takento include, but not be limited to, one or more tangible (e.g.,non-transitory) data repositories in the form of a solid-state memory,an optical medium, a magnetic medium, or any suitable combinationthereof.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute softwaremodules (e.g., code stored or otherwise embodied on a machine-readablemedium or in a transmission medium), hardware modules, or any suitablecombination thereof. A “hardware module” is a tangible (e.g.,non-transitory) unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, and such a tangible entity may bephysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software(e.g., a software module) may accordingly configure one or moreprocessors, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. As used herein,“processor-implemented module” refers to a hardware module in which thehardware includes one or more processors. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain operations may be distributed among the oneor more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

It will be understood that, although the terms “first,” “second,” etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another.

What is claimed is:
 1. A computer-implemented method, comprising:receiving a size match request associated with a cross bordertransaction involving a target garment and a website in a firstlanguage; generating, by at least one processor, a map of a base clientsize to a set of target garment sizes using a standardized size scaleand target garment information; generating, by the at least oneprocessor based on the map, a size match communication indicating afirst target garment size having a closest match between the base clientsize and a target garment size of the set of target garment sizes, thesize match communication comprising instructions, in a second languagedifferent from the first language, for selecting the target garment inthe first target garment size having the closest match from the websitein the first language; causing communication, to a user system, of thesize match communication; receiving, via the user system, a selection ofthe target garment in the first target garment size having the closestmatch; and initiating, in response to the selection, an order of thetarget garment in the first target garment size having the closest matchusing the website in the first language.
 2. The method of claim 1,wherein: the base client size is associated with a first country; andthe target garment information is associated with a second country thatis different from the first country.
 3. The method of claim 1, whereinthe base client size is associated with a garment purchase history. 4.The method of claim 3, wherein the base client size is a preferred sizedetermined based on analyzing one or more returned garment sizesassociated with the garment purchase history.
 5. The method of claim 3,wherein the base client size is determined by: identifying a brandassociated with the garment purchase history; mapping brand sizes forthe brand to the standardized size scale; and identifying the baseclient size using the standardized size scale.
 6. The method of claim 3,wherein the garment purchase history is generated automatically by apurchase history module operating on a client device recording a historyof client purchases.
 7. The method of claim 3, wherein the garmentpurchase history is generated by a user entering a brand and sizehistory for one or more garments.
 8. The method of claim 3, wherein thebase client size is determined by: identifying computer-aided design(CAD) files for client garments associated with the garment purchasehistory; and determining the base client size by analyzing dimensions ofthe client garments from the CAD files.
 9. The method of claim 1,wherein the base client size is determined by: generating a firstpartial shape of a garment based on a first image depicting a first viewof the garment; generating a second partial shape of the garment basedon a second image depicting a second view of the garment; determining atype of garment by comparing the first partial shape and the secondpartial shape to a database of reference garment shapes; generating athree-dimensional garment model by joining the first partial shape andthe second partial shape based on the determined type of garment, thegenerated three-dimensional garment model including a first group ofvertices; and determining the base client size from the generatedthree-dimensional garment model.
 10. The method of claim 9, furthercomprising: tessellating the generated three-dimensional garment modelinto triangles, wherein vertices in the triangles correspond to pointsthat are interior to the generated three-dimensional garment model,wherein a triangle among the triangles of the tessellatedthree-dimensional garment model has a minimum angle.
 11. The method ofclaim 9, further comprising: determining the base client size using areference object placed near the garment in the first image.
 12. Themethod of claim 1, wherein: the set of target garment sizes isassociated with a garment database that comprises a set of garmentmodels; and each garment model of the set of garment models isassociated with a plurality of sizes.
 13. The method of claim 12,wherein: the set of garment models comprises one or more garment modelsfor the target garment; and the one or more garment models for thetarget garment comprise the set of target garment sizes.
 14. The methodof claim 13, wherein generating the map of the base client size to theset of target garment sizes comprises analyzing the one or more garmentmodels for the target garment to associate the standardized size scaleand the base client size.
 15. The method of claim 1, wherein generatingthe map of the base client size to the set of target garment sizescomprises analyzing one or more computer-aided design (CAD) filesassociated with the target garment to identify: the standardized sizescale, wherein the standardized size scale is associated with the baseclient size; and the set of target garment sizes, wherein the targetgarment sizes are detailed by the one or more CAD files associated withthe target garment.
 16. The method of claim 1, wherein generating themap of the base client size to the set of target garment sizes comprisesanalyzing purchase histories for a plurality of users to: associate thebase client size with a first set of purchase history purchases;associate the target garment with a second set of purchase historypurchases; and evaluate the first set of purchase history purchases andat least a portion of the second set of purchase history purchases toidentify the first target garment size of the set of target garmentsizes.
 17. The method of claim 1, wherein the target garment informationcomprises a target country without identifying a particular garmentitem.
 18. The method of claim 1, wherein: the size match requestcomprises a set of shape and style preferences; and the method furthercomprises: analyzing a garment database to identify a plurality ofgarments having a threshold similarity to the set of shape and stylepreferences to identify the set of target garment sizes; and translatingat least a portion of information associated with the target garmentfrom the first language to the second language.
 19. A system comprising:at least one processor; and memory storing instructions that, whenexecuted by the at least one processor, cause the system to perform aset of operations, the set of operations comprising: receiving a sizematch request associated with a cross border transaction involving atarget garment and a website in a first language; generating a map of abase client size to a set of target garment sizes using a standardizedsize scale and target garment information; generating, based on the map,a size match communication indicating a first target garment size havinga closest match between the base client size and a target garment sizeof the set of target garment sizes, the size match communicationcomprising instructions, in a second language different from the firstlanguage, for selecting the target garment in the first target garmentsize having the closest match from the website in the first language;causing communication, to a user system, of the size matchcommunication; receiving, via the user system, a selection of the targetgarment in the first target garment size having the closest match; andinitiating, in response to the selection, an order of the target garmentin the first target garment size having the closest match using thewebsite in the first language.
 20. A non-transitory machine-readablestorage comprising instructions that, when executed by one or moreprocessors of a machine, cause the machine to perform operationscomprising: receiving a size match request associated with a targetgarment and a website in a first language; generating a map of a baseclient size to a set of target garment sizes using a standardized sizescale and target garment information; generating, based on the map, asize match communication indicating a first target garment size having aclosest match between the base client size and a target garment size ofthe set of target garment sizes, the size match communication comprisinginstructions, in a second language different from the first language,for selecting the target garment in the first target garment size havingthe closest match from the website in the first language; causingcommunication, to a user system, of the size match communication;receiving, via the user system, a selection of the target garment in thefirst target garment size having the closest match; and initiating, inresponse to the selection, an order of the target garment in the firsttarget garment size having the closest match using the website in thefirst language.
 21. The non-transitory machine-readable storage of claim20, wherein: the base client size is associated with a first country;and the target garment information is associated with a second countrythat is different from the first country.
 22. The non-transitorymachine-readable storage of claim 20, wherein the base client size isassociated with a garment purchase history.
 23. The non-transitorymachine-readable storage of claim 22, wherein the base client size is apreferred size determined based on analyzing one or more returnedgarment sizes associated with the garment purchase history.
 24. Thenon-transitory machine-readable storage of claim 22, wherein todetermine the base client size the instructions further cause themachine to perform operations comprising: identifying a brand associatedwith the garment purchase history; mapping brand sizes for the brand tothe standardized size scale; and identifying the base client size usingthe standardized size scale.
 25. The non-transitory machine-readablestorage of claim 22, wherein the garment purchase history is generatedautomatically by a purchase history module operating on a client devicerecording a history of client purchases.
 26. The non-transitorymachine-readable storage of claim 22, wherein the garment purchasehistory is generated by a user entering a brand and size history for oneor more garments.
 27. The non-transitory machine-readable storage ofclaim 22, wherein to determine the base client size the instructionsfurther cause the machine to perform operations comprising: identifyingcomputer-aided design (CAD) files for client garments associated withthe garment purchase history; and determining the base client size byanalyzing dimensions of the client garments from the CAD files.
 28. Thenon-transitory machine-readable storage of claim 20, wherein todetermine the base client size the instructions further cause themachine to perform operations comprising: generating a first partialshape of a garment based on a first image depicting a first view of thegarment; generating a second partial shape of the garment based on asecond image depicting a second view of the garment; determining a typeof garment by comparing the first partial shape and the second partialshape to a database of reference garment shapes; generating athree-dimensional garment model by joining the first partial shape andthe second partial shape based on the determined type of garment, thegenerated three-dimensional garment model including a first group ofvertices; and determining the base client size from the generatedthree-dimensional garment model.
 29. The non-transitory machine-readablestorage of claim 28, wherein the instructions further cause the machineto perform operations comprising: tessellating the generatedthree-dimensional garment model into triangles, wherein vertices in thetriangles correspond to points that are interior to the generatedthree-dimensional garment model, wherein a triangle among the trianglesof the tessellated three-dimensional garment model has a minimum angle.30. The non-transitory machine-readable storage of claim 28, wherein theinstructions further cause the machine to perform operations comprising:determining the base client size using a reference object placed nearthe garment in the first image.
 31. The non-transitory machine-readablestorage of claim 20, wherein: the set of target garment sizes isassociated with a garment database that comprises a set of garmentmodels; and each garment model of the set of garment models isassociated with a plurality of sizes.
 32. The non-transitorymachine-readable storage of claim 31, wherein: the set of garment modelscomprises one or more garment models for the target garment; and the oneor more garment models for the target garment comprise the set of targetgarment sizes.
 33. The non-transitory machine-readable storage of claim32, wherein generating the map of the base client size to the set oftarget garment sizes comprises analyzing the one or more garment modelsfor the target garment to associate the standardized size scale and thebase client size.
 34. The non-transitory machine-readable storage ofclaim 20, wherein to generate the map of the base client size to the setof target garment sizes the instructions further cause the machine toperform operations comprising analyzing one or more computer-aideddesign (CAD) files associated with the target garment to identify: thestandardized size scale, wherein the standardized size scale isassociated with the base client size; and the set of target garmentsizes, wherein the target garment sizes are detailed by the one or moreCAD files associated with the target garment.
 35. The non-transitorymachine-readable storage of claim 20, wherein to generate the map of thebase client size to the set of target garment sizes the instructionsfurther cause the machine to perform operations comprising analyzingpurchase histories for a plurality of users to: associate the baseclient size with a first set of purchase history purchases; associatethe target garment with a second set of purchase history purchases; andevaluate the first set of purchase history purchases and at least aportion of the second set of purchase history purchases to identify thefirst target garment size of the set of target garment sizes.
 36. Thenon-transitory machine-readable storage of claim 20, wherein the targetgarment information comprises a target country without identifying aparticular garment item.
 37. The non-transitory machine-readable storageof claim 20, wherein: the size match request comprises a set of shapeand style preferences; and wherein the instructions further cause themachine to perform operations comprising: analyzing a garment databaseto identify a plurality of garments having a threshold similarity to theset of shape and style preferences to identify the set of target garmentsizes; and translating at least a portion of information associated withthe target garment from the first language to the second language.